Browsing College of Arts and Sciences (MU) by Thesis Advisor "Holan, Scott H."
Now showing items 1-6 of 6
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Bayesian and machine learning models for dependent data with applications to official statistics and survey methodology
(University of Missouri--Columbia, 2023)[EMBARGOED UNTIL 8/1/2024] Small Area estimation has garnered much interest in recent times by both private entities as well government agencies as means of public policy guidance, formulating programs for regional and ... -
Bayesian unit-level modeling of non-Gaussian survey data under informative sampling with application to small area estimation
(University of Missouri--Columbia, 2021)Unit-level models are an alternative to the traditional area-level models used in small area estimation, characterized by the direct modeling of survey responses rather than aggregated direct estimates. These unit-level ... -
Decision theory and sampling algorithms for spatial and spatio-temporal point processes
(University of Missouri--Columbia, 2019)In this work, we first present a flexible hierarchical Bayesian model and develop a comprehensive Bayesian decision theoretic framework for point process theory. Then, we provide a comparative study of the approximate ... -
Hierarchical nonlinear, multivariate, and spatially-dependent time-frequency functional methods
(University of Missouri--Columbia, 2013)Notions of time and frequency are important constituents of most scientific inquiries, providing complimentary information. In an era of "big data," methodology for analyzing functional and/or image data is increasingly ... -
Modeling gibbs point processes through basic function decompositions
(University of Missouri--Columbia, 2019)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] We consider non-homogeneous pairwise interaction point process models, where the global and local effect functions are modeled using basis function ... -
Nonstationary Bayesian time series models with time-varying parameters and regime-switching
(University of Missouri--Columbia, 2021)Nonstationary time series data exist in various scientic disciplines, including environmental science, biology, signal processing, econometrics, among others. Many Bayesian models have been developed to handle nonstationary ...